seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
m_init, v_init = tf.nn.moments(x, [0])
scale_init = init_scale / tf.sqrt(v_init + 1e-10)
| tensorflow.sqrt | 10,400 |
import tensorflow as tf
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (eval_batch_size,))
def testLogitsNotSqueezed(self):
num_classes = 25
| tensorflow.random_uniform | 10,401 |
import tensorflow as tf
# image_size,
# num_channels
# )
variance = tf.reduce_mean(
input_tensor=tf.square(x=centered_image),
axis=0,
keepdims=True,
name="variance"
| tensorflow.square | 10,402 |
import tensorflow as tf
# shape is statically known.
self.assertEqual(2, a.shape[0].value)
assertions_triggered[0] += 1
return a
f0(tf.constant([1]))
f1(tf.constant([1]))
f2(tf.constant([1]))
self.assertEqual(3, assertions_triggered[0])
def test_out_of_order_execution1(self):
with self.test_session() as session:
| tensorflow.constant | 10,403 |
import tensorflow as tf
outputs = tf.nn.conv3d(inputs, kernel,
[1, stride_d, stride_h, stride_w, 1],
padding=padding)
biases = _variable_on_cpu('biases', [num_output_channels],
tf.constant_initializer(0.0))
outputs = tf.nn.bias_add(outputs, biases)
if bn:
outputs = batch_norm_for_conv3d(outputs, is_training,
bn_decay=bn_decay, scope='bn')
| tensorflow.nn.bias_add | 10,404 |
from tensorflow.python.framework import ops
self._lr_t = None
self._beta1_t = None
self._beta2_t = None
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
def _create_slots(self, var_list):
# Create slots for the first and second moments.
for v in var_list:
| tensorflow.python.framework.ops.convert_to_tensor | 10,405 |
import tensorflow as tf
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
tf.summary.scalar("%s_nonpadding_tokens" % k, nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
_already_logged = set()
| tensorflow.reduce_mean | 10,406 |
import tensorflow as tf
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver()
# create two summary writers to show training loss and validation loss in the same graph
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
validation_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validation')
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
| tensorflow.summary.FileWriter | 10,407 |
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import array_ops
import numpy as np
def safe_get(name, *args, **kwargs):
""" Same as tf.get_variable, except flips on reuse_variables automatically """
try:
return tf.get_variable(name, *args, **kwargs)
except ValueError:
tf.get_variable_scope().reuse_variables()
return tf.get_variable(name, *args, **kwargs)
def init_weights(shape, name=None):
shape = tuple(shape)
weights = np.random.normal(scale=0.01, size=shape).astype('f')
return safe_get(name, list(shape), initializer=tf.constant_initializer(weights), dtype=tf.float32)
def init_bias(shape, name=None):
return safe_get(name, initializer=tf.zeros(shape, dtype=tf.float32))
def init_fc_weights_xavier(shape, name=None):
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=tf.float32)
| tensorflow.get_variable_scope | 10,408 |
import tensorflow as tf
if self.options.add_first_word_prob_for_phrase: # add prob of the first word to each phrase
attn_dist = add_first_word_prob_to_atten_dists(self.in_passage_words, self.phrase_starts,
vocab_dist, attn_dist)
# match attn_dist[batch_size, passage_length] to sparse one-hot representation [batch_size, passage_length, extended_vsize]
batch_nums = tf.range(0, limit=batch_size) # shape (batch_size)
batch_nums = tf.expand_dims(batch_nums, axis=1) # shape (batch_size, 1)
batch_nums = tf.tile(batch_nums, [1, passage_length]) # shape (batch_size, passage_length)
step_nums = tf.range(0, limit=passage_length) # [passage_length]
step_nums = tf.expand_dims(step_nums, axis=0) # shape (1, passage_length)
step_nums = tf.tile(step_nums, [batch_size, 1]) # shape (batch_size, passage_length)
indices = tf.stack((batch_nums, step_nums, passage_word_idx), axis=2) # shape (batch_size, passage_length, 3)
indices = tf.reshape(indices, [-1, 3]) #[batch_size * passage_length, 3]
indices = tf.cast(indices, tf.int64)
shape = [batch_size, passage_length, extended_vsize]
shape = tf.cast(shape, tf.int64)
attn_dist = tf.reshape(attn_dist, shape=[-1]) # [batch_size*passage_length]
one_hot_spare_rep = tf.SparseTensor(indices=indices, values=attn_dist, dense_shape=shape) # [batch_size, passage_length, extended_vsize]
if passage_mask is not None:
passage_mask = tf.expand_dims(passage_mask, axis=-1)
one_hot_spare_rep = one_hot_spare_rep * passage_mask
| tensorflow.reshape | 10,409 |
import tensorflow as tf
mask = tf.equal(mask, tf.ones_like(mask))
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]])
scores = d_layer_3_all
# Mask
| tensorflow.layers.dense | 10,410 |
import tensorflow as tf
dw = tf.matmul(
tf.transpose(x_means_hot, perm=[1, 2, 0]),
tf.transpose(x_reshaped, perm=[1, 0, 2]))
updated_ema_means = \
moving_averages.assign_moving_average(
self.ema_means, dw, self.hparams.decay,
zero_debias=False)
n = tf.reduce_sum(updated_ema_count, axis=-1, keep_dims=True)
updated_ema_count = ((updated_ema_count + self.hparams.epsilon) / (
n + 2**self.hparams.z_size * self.hparams.epsilon) * n)
updated_ema_means = updated_ema_means / tf.expand_dims(
updated_ema_count, axis=-1)
with tf.control_dependencies([e_loss]):
update_means = tf.assign(self.means, updated_ema_means)
with tf.control_dependencies([update_means]):
loss += self.hparams.beta * e_loss
else:
# Use a gradient based loss for learning the cluster centers
loss += q_loss + self.hparams.beta * e_loss
# Get the discrete latent representation
| tensorflow.expand_dims | 10,411 |
import tensorflow as tf
targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0]
return tf.nn.seq2seq.model_with_buckets(
enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq,
per_example_loss=per_example_loss)
# Now we construct the copy model.
inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)]
with tf.variable_scope("root"):
_, losses1 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=False)
# Now check that we did not accidentally set reuse.
self.assertEqual(False, tf.get_variable_scope().reuse)
# Construct one more model with per-example loss.
tf.get_variable_scope().reuse_variables()
_, losses2 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=True)
# First loss is scalar, the second one is a 1-dimensinal tensor.
self.assertEqual([], losses1[0].get_shape().as_list())
| tensorflow.variable_scope | 10,412 |
import tensorflow as tf
@pytest.fixture
def mu(session_tf):
return tf.convert_to_tensor(Datum.mu_data)
| tensorflow.convert_to_tensor | 10,413 |
import tensorflow as tf
tf.cast(tf.nn.in_top_k(logits, labels, 1), data_type))
top_5_op = tf.reduce_sum(
tf.cast(tf.nn.in_top_k(logits, labels, 5), data_type))
return (logits, top_1_op, top_5_op)
loss = loss_function(logits, labels)
params = self.variable_mgr.trainable_variables_on_device(device_num)
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in params])
weight_decay = FLAGS.weight_decay
if weight_decay is not None and weight_decay != 0.:
loss += weight_decay * l2_loss
aggmeth = tf.AggregationMethod.DEFAULT
| tensorflow.nn.l2_loss | 10,414 |
import tensorflow as tf
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
summary = xlnet_model.get_pooled_out(FLAGS.summary_type, FLAGS.use_summ_proj)
with tf.variable_scope("logits"):
logits = tf.layers.dense(summary, 1,
kernel_initializer=xlnet_model.get_initializer())
logits = tf.reshape(logits, [bsz_per_core, 4])
one_hot_target = tf.one_hot(label, 4)
per_example_loss = -tf.reduce_sum(
tf.nn.log_softmax(logits) * one_hot_target, -1)
total_loss = tf.reduce_mean(per_example_loss)
return total_loss, per_example_loss, logits
| tensorflow.reshape | 10,415 |
import tensorflow as tf
out = tf.gradients(Omega, self.W_rec)
out[0] = tf.Print(out[0], [out[0], self.W_rec, Omega], "omega grads")
out[0] = tf.verify_tensor_all_finite(out[0], "dead omega grad")
| tensorflow.Print | 10,416 |
import tensorflow as tf
d = d.apply(
tf.contrib.data.parallel_interleave(
| tensorflow.contrib.data.parallel_interleave | 10,417 |
import tensorflow as tf
def fwd_gradients_1(self, U, x):
g = tf.gradients(U, x, grad_ys=self.dummy_x1_tf)[0]
return tf.gradients(g, self.dummy_x1_tf)[0]
def net_U0(self, x):
lambda_1 = self.lambda_1
lambda_2 = tf.exp(self.lambda_2)
U = self.neural_net(x, self.weights, self.biases)
U_x = self.fwd_gradients_0(U, x)
U_xx = self.fwd_gradients_0(U_x, x)
U_xxx = self.fwd_gradients_0(U_xx, x)
F = -lambda_1*U*U_x - lambda_2*U_xxx
U0 = U - self.dt*tf.matmul(F, self.IRK_alpha.T)
return U0
def net_U1(self, x):
lambda_1 = self.lambda_1
lambda_2 = tf.exp(self.lambda_2)
U = self.neural_net(x, self.weights, self.biases)
U_x = self.fwd_gradients_1(U, x)
U_xx = self.fwd_gradients_1(U_x, x)
U_xxx = self.fwd_gradients_1(U_xx, x)
F = -lambda_1*U*U_x - lambda_2*U_xxx
U1 = U + self.dt*tf.matmul(F, (self.IRK_beta - self.IRK_alpha).T)
return U1
| tensorflow.matmul | 10,418 |
import tensorflow as tf
def l1_l2_regularizer(var, weight_l1=1.0, weight_l2=1.0, name='l1_l2_regularizer'):
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
var: tensor to regularize.
weight_l1: an optional weight to modulate the l1 loss.
weight_l2: an optional weight to modulate the l2 loss.
name: Optional scope/name for op_scope.
Returns:
the l1+L2 loss op.
"""
with tf.name_scope(name):
weight_l1_t = tf.convert_to_tensor(weight_l1, dtype=var.dtype.base_dtype, name='weight_l1')
weight_l2_t = tf.convert_to_tensor(weight_l2, dtype=var.dtype.base_dtype, name='weight_l2')
reg_l1 = tf.multiply(weight_l1_t, tf.reduce_sum(tf.abs(var)), name='value_l1')
reg_l2 = tf.multiply(weight_l2_t, tf.nn.l2_loss(var), name='value_l2')
return tf.add(reg_l1, reg_l2, name='value')
def l1_regularizer(scale, name='l1_regularizer'):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
name: An optional name/scope name.
| tensorflow.convert_to_tensor | 10,419 |
import tensorflow as tf
# Create Clones.
clones = create_clones(config, model_fn, args, kwargs)
first_clone = clones[0]
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone.scope)
train_op = None
total_loss = None
with tf.device(config.optimizer_device()):
if optimizer:
# Place the global step on the device storing the variables.
| tensorflow.get_collection | 10,420 |
import tensorflow as tf
class_predictions_with_background: A float tensor of shape
[batch_size, 1, num_classes + 1] representing the class
predictions for the proposals.
Raises:
ValueError: if num_predictions_per_location is not 1.
"""
if num_predictions_per_location != 1:
raise ValueError('Currently RfcnBoxPredictor only supports '
'predicting a single box per class per location.')
batch_size = tf.shape(proposal_boxes)[0]
num_boxes = tf.shape(proposal_boxes)[1]
def get_box_indices(proposals):
proposals_shape = proposals.get_shape().as_list()
if any(dim is None for dim in proposals_shape):
proposals_shape = tf.shape(proposals)
ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32)
multiplier = tf.expand_dims(
tf.range(start=0, limit=proposals_shape[0]), 1)
return tf.reshape(ones_mat * multiplier, [-1])
net = image_features
| tensorflow.shape | 10,421 |
import tensorflow as tf
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
# tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif)
| tensorflow.cast | 10,422 |
import tensorflow as tf
self.validateMoments([10**5], -5.0, 1.0, 2.0, np.infty)
def testSmallStddev(self):
self.validateKolmogorovSmirnov([10**5], 0.0, 0.1, 0.05, 0.10)
class ParameterizedTruncatedNormalGpuTest(ParameterizedTruncatedNormalTest):
_use_gpu = True
# Benchmarking code
def parameterized_vs_naive(shape, num_iters, use_gpu=False):
np.random.seed(1618) # Make it reproducible.
# No CSE/CF.
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)
config = tf.ConfigProto(
graph_options=tf.GraphOptions(optimizer_options=optimizer_options))
with tf.Session(config=config) as sess:
with tf.device("/cpu:0" if not use_gpu else None):
param_op = tf.group(random_ops.parameterized_truncated_normal(shape))
naive_op = tf.group(random_ops.truncated_normal(shape))
# Burn-in to avoid session setup costs in the timing.
sess.run(param_op)
sess.run(param_op)
param_dt = timeit.timeit(lambda: sess.run(param_op), number=num_iters)
sess.run(naive_op)
sess.run(naive_op)
| tensorflow.OptimizerOptions | 10,423 |
import tensorflow as tf
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
| tensorflow.logging.info | 10,424 |
import tensorflow as tf
with tf.name_scope('validate'):
x, y = self._build_data_pipeline()
y_hat, loss = self._build_validation_model(x, y)
with tf.control_dependencies([update_op]):
return tf.print('expect', loss, y, y_hat, summarize=50)
class DataOwner:
BATCH_SIZE = 30
| tensorflow.print | 10,425 |
import tensorflow as tf
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testAttentionDecoder2(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
| tensorflow.constant_initializer | 10,426 |
import tensorflow as tf
def _DoPredictions(self, in_size, mats, class_weights=None):
"""Takes in an array of states and calculates predictions.
Get the cross-entropy for each example in the vector self._xent.
Args:
in_size: size of the hidden state vectors
mats: list of hidden state vectors
"""
pred_mat = tf.get_variable('pred_mat',
[in_size, self._out_vocab_size])
pred_bias = tf.get_variable('pred_bias', [self._out_vocab_size])
# Make a prediction on every word.
def GetWordPred(o_):
logits = tf.nn.xw_plus_b(o_, pred_mat, pred_bias)
return tf.nn.softmax(logits)
self.preds_by_word = tf.pack([GetWordPred(o_) for o_ in mats])
self.cs = self._mask / tf.reduce_sum(self._mask, 1, keep_dims=True)
# The final prediction is the average of the predictions for each word
| tensorflow.get_variable | 10,427 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# final convolutional layer
#removed GOAL_SIZE
conv4 = Conv2D(padding="valid", filters=RNN_SIZE-loc_layer_size, kernel_size=[2, 2], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=None)(pool3)
# FC layers
flat1a = Flatten(data_format='channels_last')(conv4)
#removed GOAL_SIZE
flat1b = Dense(units=RNN_SIZE-loc_layer_size)(flat1a)
# FC layers for goal_pos input
# goal_layer1 = Dense(units=GOAL_SIZE)(goal_pos)
# goal_layer2 = Dense(units=GOAL_SIZE)(goal_layer1)
# FC layers to find next location
loc_layer1 = Dense(units=loc_layer_size)(prev_loc)
| tensorflow.keras.layers.Dense | 10,428 |
import tensorflow as tf
logits = self.tanh_constant * tf.tanh(logits)
index = tf.multinomial(logits, 1)
index = tf.to_int32(index)
index = tf.reshape(index, [1])
| tensorflow.to_int32 | 10,429 |
import tensorflow as tf
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
deterministic_actions = tf.argmax(q_values, axis=1)
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
| tensorflow.stack | 10,430 |
import tensorflow as tf
with tf.variable_scope(name) :
self.w = tf.get_variable('w',[input_dim, output_dim],
initializer=tf.random_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim],
initializer=tf.constant_initializer(0.0))
def __call__(self,input_var,name=None,w=None,b=None,**kwargs) :
w = w if w is not None else self.w
| tensorflow.constant_initializer | 10,431 |
import tensorflow as tf
initializer=tf.random_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim], initializer=tf.constant_initializer(0.0))
self.padding = [ [0,0],[k_h//2,k_h//2],[k_w//2,k_w//2],[0,0] ]
def __call__(self,input_var,name=None,**kwargs):
_,h,w,c = input_var.shape.as_list()
_t = tf.image.resize_nearest_neighbor(input_var, [h*2, w*2])
_t = tf.pad(_t,self.padding, mode='SYMMETRIC')
return tf.nn.bias_add(
tf.nn.conv2d(_t, self.w,
data_format='NHWC', #we can't use cudnn due to resize method...
strides=[1,1,1,1], padding="VALID"),
self.b,data_format='NHWC',name=name)
def get_variables(self):
return {'w':self.w,'b':self.b}
class WeightNormSymPadConv2d(object): #Resize and Convolution(upsacle by 2)
def __init__(self,name,input_dim,output_dim,
k_h=3,k_w=3,stddev=0.02) :
| tensorflow.nn.conv2d | 10,432 |
import tensorflow as tf
outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder(
attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous,
decoder_inputs=targets[:, :-1], align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length,
**parameters
)
if use_baseline:
baseline_rewards = reinforce_baseline(outputs, rewards) # FIXME: use logits or decoder outputs?
baseline_weights = get_weights(samples, utils.EOS_ID, include_first_eos=False)
baseline_loss_ = baseline_loss(rewards=baseline_rewards, weights=baseline_weights)
else:
baseline_rewards = rewards
baseline_loss_ = tf.constant(0.0)
reinforce_weights = get_weights(samples, utils.EOS_ID, include_first_eos=True)
reinforce_loss = sequence_loss(logits=outputs, targets=samples, weights=reinforce_weights,
rewards=baseline_rewards)
trg_mask = get_weights(targets[:, 1:], utils.EOS_ID, include_first_eos=True)
xent_loss = sequence_loss(logits=outputs, targets=targets[:, 1:], weights=trg_mask)
if monotonicity_weight:
monotonicity_dist = monotonicity_dist or 1.0
| tensorflow.constant | 10,433 |
import tensorflow as tf
# Get the normalized coordinates of bboxes
bottom_shape = tf.shape(bottom)
height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
# rois除以h,w就得到了rois在特征图上的位置
x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width
y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
# Won't be backpropagated to rois anyway, but to save time
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1))
# 'roi_pooling_size', 7
pre_pool_size = cfg.FLAGS.roi_pooling_size * 2
# 把rois对于的特征图上的部分crop出来，然后resize打破14*14的大小
crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops")
return slim.max_pool2d(crops, [2, 2], padding='SAME')
def _dropout_layer(self, bottom, name, ratio=0.5):
return tf.nn.dropout(bottom, ratio, name=name)
def _anchor_target_layer(self, rpn_cls_score, name):
with tf.variable_scope(name):
# 这里的index是对于所有anchor而言
# (1, 1, A * height, width)
# (1, height, width, A * 4)
# (1, height, width, A * 4)
# (1, height, width, A * 4)
| tensorflow.to_int32 | 10,434 |
import tensorflow as tf
shape
[-1, num_blocks, block_dim].
means: Embedding means.
Returns:
The nearest neighbor in one hot form, the nearest neighbor
itself, the
commitment loss, embedding training loss.
"""
x_means_hot = self.nearest_neighbor(x, means)
x_means_hot_flat = tf.reshape(
x_means_hot, [-1, self.hparams.num_blocks, self.hparams.block_v_size])
x_means = tf.matmul(tf.transpose(x_means_hot_flat, perm=[1, 0, 2]), means)
x_means = tf.transpose(x_means, [1, 0, 2])
q_loss = tf.reduce_mean(
tf.squared_difference(tf.stop_gradient(x), x_means))
e_loss = tf.reduce_mean(
tf.squared_difference(x, tf.stop_gradient(x_means)))
return x_means_hot, x_means, q_loss, e_loss
| tensorflow.reshape | 10,435 |
import tensorflow as tf
d_label_smooth = self.cnf['d_label_smooth'] # 0.25
self.d_loss_real = self._sigmoid_kl_with_logits(self.end_points_D['D_on_data_logits'],
1. - d_label_smooth)
class_loss_weight = 1.
self.d_loss_class = class_loss_weight * tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.end_points_D['class_logits'], labels=tf.to_int64(targets))
self.test_loss = 1. - \
tf.reduce_mean(tf.to_float(tf.nn.in_top_k(
self.end_points_D_val['logits'], targets, 1)))
self.error_rate = 1. - \
tf.reduce_mean(tf.to_float(tf.nn.in_top_k(
self.end_points_D['class_logits'], targets, 1)))
if gpu_idx == 0:
update = tf.assign(num_error_rate, num_error_rate + 1.)
with tf.control_dependencies([update]):
tc = tf.maximum(.01, 1. / num_error_rate)
update = tf.assign(avg_error_rate, (1. - tc) * avg_error_rate + tc * self.error_rate)
with tf.control_dependencies([update]):
self.d_loss_class = tf.identity(self.d_loss_class)
self.d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(
logits=self.end_points_D['D_on_G_logits'],
labels=tf.zeros_like(self.end_points_D['D_on_G_logits']))
self.d_loss_class = tf.reduce_mean(self.d_loss_class)
self.d_loss_real = tf.reduce_mean(self.d_loss_real)
self.d_loss_fake = tf.reduce_mean(self.d_loss_fake)
if is_fm_loss:
global_pool_head = self.end_points_D['global_pool']
real_data_features = tf.slice(global_pool_head, [0, 0], [batch_size_train, num_classes])
fake_data_features = tf.slice(global_pool_head, [batch_size_train, 0],
| tensorflow.maximum | 10,436 |
import tensorflow as tf
else:
nfeats_conv = reduce(lambda x, y: x * y, [int(x) for x in cnn_output.get_shape()[-3:]])
feats_conv = tf.reshape(cnn_output, [batch_size * rnn_nunroll, nfeats_conv])
nfeats_tot = nfeats_conv + nfeats
feats_all = tf.concat(1, [feats_conv, feats_other])
print('feats_cnn: {}'.format(feats_conv.get_shape()))
print('feats_all: {}'.format(feats_all.get_shape()))
# Project to RNN size
rnn_output = feats_all
rnn_output_size = nfeats_tot
if do_rnn:
with tf.variable_scope('rnn_proj'):
rnn_proj_w = tf.get_variable('W', [nfeats_tot, rnn_size], initializer=tf.uniform_unit_scaling_initializer(factor=1.0, dtype=dtype), dtype=dtype)
rnn_proj_b = tf.get_variable('b', [rnn_size], initializer=tf.constant_initializer(0.0), dtype=dtype)
rnn_inputs = tf.nn.bias_add(tf.matmul(feats_all, rnn_proj_w), rnn_proj_b)
rnn_inputs = tf.reshape(rnn_inputs, [batch_size, rnn_nunroll, rnn_size])
rnn_inputs = tf.split(rnn_inputs, rnn_nunroll, axis=1)
rnn_inputs = [tf.squeeze(input_, [1]) for input_ in rnn_inputs]
if rnn_cell_type == 'rnn':
cell_fn = tf.nn.rnn_cell.BasicRNNCell
elif rnn_cell_type == 'gru':
cell_fn = tf.nn.rnn_cell.GRUCell
elif rnn_cell_type == 'lstm':
cell_fn = tf.nn.rnn_cell.BasicLSTMCell
else:
| tensorflow.constant_initializer | 10,437 |
import tensorflow as tf
import os
log = infolog.log
def add_embedding_stats(summary_writer, embedding_names, paths_to_meta, checkpoint_path):
# Create tensorboard projector
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
config.model_checkpoint_path = checkpoint_path
for embedding_name, path_to_meta in zip(embedding_names, paths_to_meta):
# Initialize config
embedding = config.embeddings.add()
# Specifiy the embedding variable and the metadata
| tensorflow.contrib.tensorboard.plugins.projector.ProjectorConfig | 10,438 |
import tensorflow as tf
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
| tensorflow.train.init_from_checkpoint | 10,439 |
from tensorflow.python.ops import math_ops
logits = self._logits(features)
return self._logits_to_predictions(logits, proba=True)
def _logits_to_predictions(self, logits, proba=False):
if self._n_classes < 2:
return array_ops.reshape(logits, [-1])
if self._n_classes == 2:
logits = array_ops.concat(1, [array_ops.zeros_like(logits), logits])
if proba:
return nn.softmax(logits)
else:
return math_ops.argmax(logits, 1)
def _get_feature_ops_from_example(self, examples_batch):
column_types = layers.create_dict_for_parse_example(
(self._get_linear_feature_columns() or []) +
(self._get_dnn_feature_columns() or []))
features = parsing_ops.parse_example(examples_batch, column_types)
return features
def _num_label_columns(self):
return 1 if self._n_classes <= 2 else self._n_classes
def _get_linear_feature_columns(self):
| tensorflow.python.ops.math_ops.argmax | 10,440 |
import tensorflow as tf
act: (tf.Variable, bool, float, bool, float, bool) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
"""
if param_noise_filter_func is None:
param_noise_filter_func = default_param_noise_filter
with tf.variable_scope(scope, reuse=reuse):
observations_ph = make_obs_ph("observation")
stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
update_param_noise_threshold_ph = tf.placeholder(tf.float32, (), name="update_param_noise_threshold")
update_param_noise_scale_ph = tf.placeholder(tf.bool, (), name="update_param_noise_scale")
reset_ph = tf.placeholder(tf.bool, (), name="reset")
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
param_noise_scale = tf.get_variable("param_noise_scale", (), initializer=tf.constant_initializer(0.01), trainable=False)
param_noise_threshold = tf.get_variable("param_noise_threshold", (), initializer=tf.constant_initializer(0.05), trainable=False)
# Unmodified Q.
q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
# Perturbable Q used for the actual rollout.
q_values_perturbed = q_func(observations_ph.get(), num_actions, scope="perturbed_q_func")
| tensorflow.placeholder | 10,441 |
import tensorflow as tf
dec, mem = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, num_decoder_symbols=3,
embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2,
output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
| tensorflow.get_variable | 10,442 |
import tensorflow as tf
self.sample_entropy = entropy_1 + entropy_2
self.sample_log_prob = log_prob_1 + log_prob_2
def _create_params(self):
initializer = tf.random_uniform_initializer(minval=-0.1, maxval=0.1)
with tf.variable_scope(self.name, initializer=initializer):
with tf.variable_scope("lstm"):
self.w_lstm = []
for layer_id in range(self.lstm_num_layers):
with tf.variable_scope("layer_{}".format(layer_id)):
w = tf.get_variable("w", [2 * self.lstm_size, 4 * self.lstm_size])
self.w_lstm.append(w)
self.g_emb = tf.get_variable("g_emb", [1, self.lstm_size])
with tf.variable_scope("emb"):
self.w_emb = tf.get_variable("w", [self.num_branches, self.lstm_size])
with tf.variable_scope("softmax"):
self.w_soft = tf.get_variable("w", [self.lstm_size, self.num_branches])
b_init = np.array([10.0, 10.0] + [0] * (self.num_branches - 2),
dtype=np.float32)
self.b_soft = tf.get_variable(
"b", [1, self.num_branches],
initializer=tf.constant_initializer(b_init))
b_soft_no_learn = np.array(
[0.25, 0.25] + [-0.25] * (self.num_branches - 2), dtype=np.float32)
| tensorflow.get_variable | 10,443 |
import tensorflow as tf
'model_scope', None,
'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
'checkpoint_exclude_scopes', None,
'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
'ignore_missing_vars', True,
'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
'run_on_cloud', False,
'Wether we will train on cloud.')
tf.app.flags.DEFINE_boolean(
'seq_train', False,
'Wether we will train a sequence model.')
tf.app.flags.DEFINE_string(#
'model_to_train', 'blouse, dress, outwear, skirt, trousers', #'all, blouse, dress, outwear, skirt, trousers', 'skirt, dress, outwear, trousers',
| tensorflow.app.flags.DEFINE_boolean | 10,444 |
from tensorflow.python.ops import array_ops
def _log_loss_with_two_classes(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
loss_vec = nn.sigmoid_cross_entropy_with_logits(logits,
math_ops.to_float(target))
return loss_vec
| tensorflow.python.ops.array_ops.expand_dims | 10,445 |
import tensorflow as tf
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.summary.histogram(var.op.name, var))
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
cifar10.MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op, variables_averages_op)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.summary.merge(summaries)
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
| tensorflow.group | 10,446 |
from tensorflow.python.framework import ops
print("%s takes %.4f sec/step" % (desc, step_time))
self.report_benchmark(
name=desc, iters=benchmark_steps, wall_time=total_time)
def benchmarkCudnnLSTMTraining(self):
test_configs = self._GetTestConfig()
for config_name, config in test_configs.items():
config = test_configs[config_name]
num_layers = config["num_layers"]
num_units = config["num_units"]
batch_size = config["batch_size"]
seq_length = config["seq_length"]
with ops.Graph().as_default(), ops.device("/device:GPU:0"):
model = cudnn_rnn_ops.CudnnLSTM(num_layers, num_units, num_units)
params_size_t = model.params_size()
input_data = variables.Variable(
array_ops.ones([seq_length, batch_size, num_units]))
input_h = variables.Variable(
array_ops.ones([num_layers, batch_size, num_units]))
input_c = variables.Variable(
array_ops.ones([num_layers, batch_size, num_units]))
params = variables.Variable(
array_ops.ones([params_size_t]), validate_shape=False)
output, output_h, output_c = model(
is_training=True,
| tensorflow.python.framework.ops.Graph | 10,447 |
import tensorflow as tf
cell = DropoutWrapper(cell, input_keep_prob=encoder.rnn_input_keep_prob,
output_keep_prob=encoder.rnn_output_keep_prob,
state_keep_prob=encoder.rnn_state_keep_prob,
variational_recurrent=encoder.pervasive_dropout,
dtype=tf.float32, input_size=input_size)
return cell
batch_size = tf.shape(encoder_inputs_)[0]
time_steps = tf.shape(encoder_inputs_)[1]
if embeddings is not None:
flat_inputs = tf.reshape(encoder_inputs_, [tf.multiply(batch_size, time_steps)])
flat_inputs = tf.nn.embedding_lookup(embeddings, flat_inputs)
encoder_inputs_ = tf.reshape(flat_inputs,
tf.stack([batch_size, time_steps, flat_inputs.get_shape()[1].value]))
if pos_embeddings is not None:
pos_inputs_ = tf.range(time_steps, dtype=tf.int32)
pos_inputs_ = tf.nn.embedding_lookup(pos_embeddings, pos_inputs_)
pos_inputs_ = tf.tile(tf.expand_dims(pos_inputs_, axis=0), [batch_size, 1, 1])
encoder_inputs_ = tf.concat([encoder_inputs_, pos_inputs_], axis=2)
if other_inputs is not None:
encoder_inputs_ = tf.concat([encoder_inputs_, other_inputs], axis=2)
| tensorflow.nn.embedding_lookup | 10,448 |
from tensorflow.core.framework import op_def_pb2
def _to_argdef_list(args):
names = [n for n, t in args]
if len(names) != len(set(names)):
raise ValueError("Expected names to all be unique: %s" % str(names))
return [op_def_pb2.OpDef.ArgDef(type=t.as_datatype_enum, name=n)
for n, t in args]
self._sig.input_arg.extend(_to_argdef_list(inputs))
self._sig.output_arg.extend(_to_argdef_list(outputs))
| tensorflow.core.framework.op_def_pb2.OpDef.ArgDef | 10,449 |
import tensorflow as tf
else:
d = tf.data.TFRecordDataset(input_files)
| tensorflow.data.TFRecordDataset | 10,450 |
import tensorflow as tf
elif self.optim_type == 'rprop':
self.optimizer = tf.train.RMSPropOptimizer(self.lr)
| tensorflow.train.RMSPropOptimizer | 10,451 |
import tensorflow as tf
def _TokenToString(self, token):
return py_x_ops.vocab_id_to_token(token, vocab=self._pieces)
def _StringToToken(self, tokstr):
return tf.where(
py_x_ops.token_in_vocab(tokstr, vocab=self._pieces),
py_x_ops.vocab_token_to_id(tokstr, vocab=self._pieces),
tf.broadcast_to(NO_TOKEN, tf.shape(tokstr)))
def _MergeTokens(self, tokens):
return self._StringToToken(
self._TokenToString(tokens[0]) + self._TokenToString(tokens[1]))
def _EncodeToIds(self, word):
# Below:
| tensorflow.shape | 10,452 |
import tensorflow as tf
window = [1,patch_size,patch_size,1]
print('Window:',window)
n_row,n_col,n_channel = x.shape
n_patch = n_row*n_col // (patch_size**2)
patches = tf.image.extract_patches(tf.expand_dims(x,0),sizes=window,strides=window,rates=[1, 1, 1, 1],padding='VALID')
patches = tf.reshape(patches,[n_patch,patch_size,patch_size,n_channel])
patches = tf.random.shuffle(patches)
rows = tf.split(patches,n_col//patch_size,axis=0)
rows = [tf.concat(tf.unstack(x),axis=1) for x in rows]
x_aug = tf.concat(rows,axis=0)
x_aug = tf.convert_to_tensor(x_aug)
return tf.concat([x, x_aug],axis=2)
def gaussian_blur(self,x):
#create random gaussian blur filter
mean = 0
std = tf.random.uniform(shape=[],minval=5,maxval=10,dtype=tf.float32) # std [5-10]
size = tf.random.uniform(shape=[],minval=3,maxval=7,dtype=tf.int32) # size [7-15]
self.kernel = self.gaussian_kernel(size,mean,std)
self.kernel = tf.tile(self.kernel[:, :, tf.newaxis, tf.newaxis], [1, 1, 3, 1])
| tensorflow.convert_to_tensor | 10,453 |
import tensorflow as tf
"""
TODO: Track Moving mean and variance, and use this statistics.
with tf.variable_scope(name):
self.moving_mean = tf.get_variable('moving_mean',[dims], initializer=tf.constant_initializer(0.0), trainable=False)
self.moving_variance = tf.get_variable('moving_variance',[dims], initializer=tf.constant_initializer(1.0), trainable=False)
"""
if out_dim is not None:
with tf.variable_scope(name) :
self.gamma= tf.get_variable('gamma',[1,1,1,out_dim], initializer=tf.constant_initializer(1.0))
self.beta = tf.get_variable('beta',[out_dim], initializer=tf.constant_initializer(0.0))
else:
self.gamma = None
self.beta = None
self.axis = axis
| tensorflow.variable_scope | 10,454 |
import tensorflow as tf
"""
smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
shape = tf.shape(image)
height = shape[0]
width = shape[1]
new_height, new_width = _smallest_size_at_least(height, width, smallest_side)
image = tf.expand_dims(image, 0)
resized_image = tf.image.resize_bilinear(image, [new_height, new_width],
align_corners=False)
resized_image = tf.squeeze(resized_image)
resized_image.set_shape([None, None, 3])
return resized_image
def preprocess_for_train(image,
output_height,
output_width,
resize_side_min=_RESIZE_SIDE_MIN,
resize_side_max=_RESIZE_SIDE_MAX):
| tensorflow.squeeze | 10,455 |
import tensorflow as tf
tf.get_variable_scope(), reuse=True))
else:
# work around https://github.com/tensorflow/tensorflow/issues/14703
ret.append(tf.variable_scope(tf.get_variable_scope()))
# always clear existing ns # TODO check existing ns
if len(self._name) and self._name != self._vs_name:
| tensorflow.get_variable_scope | 10,456 |
import tensorflow as tf
prediction = add_layer(l1, 10, 1, activation_function=None,nameScope="layerTest2")
sess = tf.Session()
# 上面的wtih或者是name都是可选的，可以选择添加，也可以选择不添加，but下面的这一行是一定要写的。
# 这个表明了 在当前的目录下面创建以恶搞logs的文件家，然后把图的信息保存进去
# 这样运行完这段代码之后，就会有一个logs的文件夹被创建
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: # tensorflow version < 0.12
writer = tf.train.SummaryWriter('logs/', sess.graph)
else: # tensorflow version >= 0.12
writer = tf.summary.FileWriter("logs/", sess.graph)
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init) | tensorflow.global_variables_initializer | 10,457 |
import tensorflow as tf
for x_val in x_vals:
print(sess.run(prod, feed_dict={x_data: x_val}))
merged = tf.merge_all_summaries()
if not os.path.exists('tensorboard_logs/'):
os.makedirs('tensorboard_logs/')
| tensorflow.merge_all_summaries | 10,458 |
import tensorflow as tf
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
prediction_dict: a dictionary holding prediction tensors to be
passed to the Loss or Postprocess functions.
"""
flattened_inputs = tf.contrib.layers.flatten(preprocessed_inputs)
class_prediction = tf.contrib.layers.fully_connected(
flattened_inputs, self._num_classes)
box_prediction = tf.contrib.layers.fully_connected(flattened_inputs, 4)
return {
'class_predictions_with_background': tf.reshape(
class_prediction, [-1, 1, self._num_classes]),
'box_encodings': tf.reshape(box_prediction, [-1, 1, 4])
}
def postprocess(self, prediction_dict, true_image_shapes, **params):
"""Convert predicted output tensors to final detections. Unused.
Args:
prediction_dict: a dictionary holding prediction tensors.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
| tensorflow.reshape | 10,459 |
import tensorflow as tf
'sync_queues_step_end_', [main_fetch_group]))
variable_mgr_post_init_ops = self.variable_mgr.get_post_init_ops()
if variable_mgr_post_init_ops:
post_init_op_group = tf.group(*variable_mgr_post_init_ops)
else:
post_init_op_group = None
| tensorflow.group | 10,460 |
import tensorflow as tf
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
| tensorflow.logging.set_verbosity | 10,461 |
import tensorflow as tf
self.assertEqual(self.evaluate(pareto.batch_shape_tensor()), (5,))
self.assertEqual(pareto.batch_shape, tf.TensorShape([5]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
self.assertEqual(pareto.event_shape, tf.TensorShape([]))
def testParetoShapeBroadcast(self):
scale = tf.constant([[3., 2.]])
concentration = tf.constant([[4.], [5.], [6.]])
pareto = tfd.Pareto(concentration, scale)
self.assertAllEqual(self.evaluate(pareto.batch_shape_tensor()), (3, 2))
self.assertAllEqual(pareto.batch_shape, tf.TensorShape([3, 2]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
| tensorflow.constant | 10,462 |
from tensorflow.python.ops import array_ops
def reallocate():
next_size = _next_array_size(new_size)
next_shape = array_ops.pack([next_size] + fixed_shape)
new_value = array_ops.zeros(next_shape, dtype=values.dtype)
old_value = array.value()
| tensorflow.python.ops.array_ops.pack | 10,463 |
import tensorflow as tf
data_batch.append(d)
timestamps_batch.append(t)
label_batch.append(l)
h5f.close()
if len(timestamps_batch) > 0:
yield np.array(data_batch), np.array(timestamps_batch), np.array(label_batch)
def cnn_bi_lstm_model(x, amp_factor, bil_lstm_win_size, num_classes):
logits = cnn_model(x, amp_factor=amp_factor)
logits = tf.reshape(logits, [-1, bil_lstm_win_size, 256*amp_factor])
forward_cell = tf.nn.rnn_cell.LSTMCell(128)
backward_cell = tf.nn.rnn_cell.LSTMCell(128)
encoder_outputs,_ = tf.nn.bidirectional_dynamic_rnn(
forward_cell,
backward_cell,
logits,
dtype=tf.float32
)
encoder_outputs = tf.concat(encoder_outputs, axis=2)
logits = tf.reshape(tf.layers.dense(encoder_outputs, units=num_classes), [-1, bil_lstm_win_size, num_classes])
| tensorflow.reshape | 10,464 |
from tensorflow.python.ops import variable_scope as vs
return
# Create the func_def object.
temp_graph = _FuncGraph()
with temp_graph.as_default():
# List of placeholders for the function_def.
inputs = []
for (argname, argtype) in self._args:
argholder = array_ops.placeholder(argtype, name=argname)
inputs.append(argholder)
# Call func and gather the output tensors.
with vs.variable_scope("", custom_getter=temp_graph.getvar):
outputs = self._func(*inputs)
# If func only returned one value, make it a tuple.
if not isinstance(outputs, (list, tuple)):
outputs = (outputs,)
if any([_ is None for _ in outputs]):
raise ValueError("Function can not return None.")
# Ensures each output is a Tensor.
outputs = [ops.convert_to_tensor(_) for _ in outputs]
self._extra_inputs = temp_graph.extra_inputs
inputs.extend(temp_graph.extra_args)
| tensorflow.python.ops.variable_scope.variable_scope | 10,465 |
import tensorflow as tf
res = sess.run([mem])
self.assertEqual(2, len(res[0]))
self.assertEqual((2, 2), res[0][0].c.shape)
self.assertEqual((2, 2), res[0][0].h.shape)
self.assertEqual((2, 2), res[0][1].c.shape)
self.assertEqual((2, 2), res[0][1].h.shape)
def testEmbeddingAttentionDecoder(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
cell = tf.nn.rnn_cell.GRUCell(2)
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
dec, mem = tf.nn.seq2seq.embedding_attention_decoder(
dec_inp, enc_state, attn_states, cell, num_symbols=4,
embedding_size=2, output_size=3)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testEmbeddingAttentionSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
| tensorflow.constant | 10,466 |
import tensorflow as tf
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
| tensorflow.summary.FileWriter | 10,467 |
import tensorflow as tf
:param padding: [string] `VALID` or `SAME`, padding method for sparse convolution.
:return [Tensor] [N, H', W', C]. Convolution results.
"""
blk_shape = tf.shape(blk_indices)
blk_indices_ = tf.reshape(blk_indices, [-1, 3])
ksize = tf.shape(w)
| tensorflow.shape | 10,468 |
import tensorflow as tf
output_ = tf.contrib.layers.layer_norm(output_, activation_fn=tf.nn.tanh, scope='output_layer_norm')
else:
output_ = dense(output_, deep_layer_size, activation=tf.tanh, use_bias=True, name='deep_output')
if decoder.use_dropout:
size = tf.shape(output_)[1]
noise_shape = [1, size] if decoder.pervasive_dropout else None
output_ = tf.nn.dropout(output_, keep_prob=decoder.deep_layer_keep_prob, noise_shape=noise_shape)
else:
if decoder.pred_maxout_layer:
maxout_size = decoder.maxout_size or cell_output_size
output_ = dense(output_, maxout_size, use_bias=True, name='maxout')
if decoder.old_maxout: # for back-compatibility with old models
output_ = tf.nn.pool(tf.expand_dims(output_, axis=2), window_shape=[2], pooling_type='MAX',
padding='SAME', strides=[2])
output_ = tf.squeeze(output_, axis=2)
else:
output_ = tf.maximum(*tf.split(output_, num_or_size_splits=2, axis=1))
if decoder.pred_embed_proj:
# intermediate projection to embedding size (before projecting to vocabulary size)
# this is useful to reduce the number of parameters, and
# to use the output embeddings for output projection (tie_embeddings parameter)
output_ = dense(output_, decoder.embedding_size, use_bias=False, name='softmax0')
if decoder.tie_embeddings and (decoder.pred_embed_proj or decoder.pred_deep_layer):
bias = get_variable('softmax1/bias', shape=[decoder.vocab_size])
output_ = tf.matmul(output_, tf.transpose(embedding)) + bias
else:
output_ = dense(output_, decoder.vocab_size, use_bias=True, name='softmax1')
| tensorflow.squeeze | 10,469 |
import tensorflow as tf
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope("cls/seq_relationship"):
output_weights = tf.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
| tensorflow.variable_scope | 10,470 |
from tensorflow.python.ops import math_ops
Raises:
ValueError: if k is invalid.
"""
if k < 1:
raise ValueError('Invalid k=%s.' % k)
with ops.name_scope(
None, 'average_precision', (predictions, labels, k)) as scope:
# Calculate top k indices to produce [D1, ... DN, k] tensor.
_, predictions_idx = nn.top_k(predictions, k)
predictions_idx = math_ops.to_int64(predictions_idx, name='predictions_idx')
# Expand dims to produce [D1, ... DN, k, 1] tensor. This gives us a separate
# prediction for each k, so we can calculate separate true positive values
# for each k.
predictions_idx_per_k = array_ops.expand_dims(
predictions_idx, -1, name='predictions_idx_per_k')
# Replicate labels k times to produce [D1, ... DN, k, num_labels] tensor.
labels_per_k = expand_and_tile(
| tensorflow.python.ops.math_ops.to_int64 | 10,471 |
import tensorflow as tf
# 減少代理損失
self.aloss = -tf.reduce_mean(tf.minimum(
surr,
tf.clip_by_value(ratio, 1. - EPSILON, 1. + EPSILON) * self.tfadv))
self.atrain_op = tf.train.AdamOptimizer(A_LR).minimize(self.aloss)
# log
self.train_writer = tf.summary.FileWriter("logs/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
self.tableAction = self.createActionTable()
def createActionTable(self):
tableAction = []
for a in range(0, 3):
for b in range(0, 3):
| tensorflow.global_variables_initializer | 10,472 |
import tensorflow as tf
shape=[num_examples, seq_length],
dtype=tf.int32),
"label_ids":
tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
})
| tensorflow.constant | 10,473 |
import tensorflow as tf
:, -1],
method=1)
tf.summary.image('Compare/gtboxes_h_gpu:%d' % i, gtboxes_in_img_h)
tf.summary.image('Compare/gtboxes_r_gpu:%d' % i, gtboxes_in_img_r)
if cfgs.ADD_BOX_IN_TENSORBOARD:
| tensorflow.summary.image | 10,474 |
import tensorflow as tf
if policy_name.startswith('svmrank'):
policy['model'] = svmrank.Model().read(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.txt")
else:
with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.pkl", 'rb') as f:
policy['model'] = pickle.load(f)
# load feature specifications
with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/feat_specs.pkl", 'rb') as f:
feat_specs = pickle.load(f)
policy['batch_datatypes'] = [tf.float32, tf.int32, tf.int32, tf.float32]
policy['batch_fun'] = lambda x: load_batch_flat(x, feat_specs['type'], feat_specs['augment'], feat_specs['qbnorm'])
test_data = tf.data.Dataset.from_tensor_slices(test_files)
test_data = test_data.batch(test_batch_size)
test_data = test_data.map(lambda x: tf.py_func(
policy['batch_fun'], [x], policy['batch_datatypes']))
test_data = test_data.prefetch(2)
test_kacc = process(policy, test_data, top_k)
print(f" {seed} " + " ".join([f"acc@{k}: {100*acc:4.1f}" for k, acc in zip(top_k, test_kacc)]))
writer.writerow({
**{
'policy': f"{policy['type']}:{policy['name']}",
'seed': seed,
},
**{
f'acc@{k}': test_kacc[i] for i, k in enumerate(top_k)
},
| tensorflow.py_func | 10,475 |
from tensorflow.python.training import gradient_descent
yield
else:
yield
def _setupDense(self, is_distributed, dtype):
with self._maybeWithDevice("/job:ps" if is_distributed else None):
var0 = variables.Variable([[0.0, 1.0], [2.0, 3.0]], dtype=dtype)
var1 = variables.Variable([4.0, 5.0], dtype=dtype)
with self._maybeWithDevice("/job:worker" if is_distributed else None):
grads0 = constant_op.constant([[0.1, 0.1], [0.1, 0.1]], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
sgd = gradient_descent.GradientDescentOptimizer(3.0)
clip_opt = variable_clipping_optimizer.VariableClippingOptimizer(
sgd, {var0: [1]}, 2.0)
update_op = clip_opt.apply_gradients(
list(zip([grads0, grads1], [var0, var1])))
variables.global_variables_initializer().run()
return var0, var1, update_op
def _assertDenseCorrect(self, var0, var1, update_op):
# Fetch params to validate initial values
| tensorflow.python.training.gradient_descent.GradientDescentOptimizer | 10,476 |
import tensorflow as tf
global_mode(),
feed_dict={tf.global_mode(): tf.estimator.ModeKeys.PREDICT})
# mode == tf.estimator.ModeKeys.PREDICT
"""
mode = tf.get_collection_ref(_GLOBAL_MODE_KEY)
if len(mode) < 1:
# mode_tensor = tf.placeholder(tf.string, name="global_mode")
mode_tensor = tf.placeholder_with_default(
input=tf.estimator.ModeKeys.TRAIN,
shape=(),
name="global_mode")
# mode_tensor = tf.constant(
# value=tf.estimator.ModeKeys.TRAIN,
# dtype=tf.string,
| tensorflow.placeholder_with_default | 10,477 |
import tensorflow as tf
B_dis_fake = self.discriminator(AB)
def LSGAN_losses(real, fake):
d_real = tf.reduce_mean(tf.squared_difference(real, 1), name='d_real')
d_fake = tf.reduce_mean(tf.square(fake), name='d_fake')
d_loss = tf.multiply(d_real + d_fake, 0.5, name='d_loss')
g_loss = tf.reduce_mean(tf.squared_difference(fake, 1), name='g_loss')
add_moving_summary(g_loss, d_loss)
return g_loss, d_loss
with tf.name_scope('losses'):
with tf.name_scope('LossA'):
# reconstruction loss
recon_loss_A = tf.reduce_mean(tf.abs(A - ABA), name='recon_loss')
# gan loss
G_loss_A, D_loss_A = LSGAN_losses(A_dis_real, A_dis_fake)
with tf.name_scope('LossB'):
recon_loss_B = tf.reduce_mean(tf.abs(B - BAB), name='recon_loss')
G_loss_B, D_loss_B = LSGAN_losses(B_dis_real, B_dis_fake)
LAMBDA = 10.0
self.g_loss = tf.add((G_loss_A + G_loss_B),
| tensorflow.name_scope | 10,478 |
import tensorflow as tf
def hw_flatten(x):
return tf.reshape(x, shape=[x.shape[0], -1, x.shape[-1]])
# N = h * w
s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # # [bs, N, N]
beta = tf.nn.softmax(s, axis=-1) # attention map
o = tf.matmul(beta, hw_flatten(h)) # [bs, N, C]
gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0))
o = tf.reshape(o, shape=inputs.shape) # [bs, h, w, C]
x = gamma * o + inputs
return x
@add_arg_scope
| tensorflow.constant_initializer | 10,479 |
import tensorflow as tf
is_train=self.is_train)
_, state = self.gru(d_inp, init)
tf.get_variable_scope().reuse_variables()
_, logits2 = pointer(d_match, state * self.dropout_mask, d, mask)
return logits1, logits2
def dropout(args, keep_prob, is_train, mode="recurrent"):
if keep_prob < 1.0:
noise_shape = None
scale = 1.0
shape = tf.shape(args)
if mode == "embedding":
noise_shape = [shape[0], 1]
scale = keep_prob
if mode == "recurrent" and len(args.get_shape().as_list()) == 3:
noise_shape = [shape[0], 1, shape[-1]]
args = tf.cond(is_train, lambda: tf.nn.dropout(
args, keep_prob, noise_shape=noise_shape) * scale, lambda: args)
return args
| tensorflow.shape | 10,480 |
from tensorflow.python.ops import math_ops
max_update = state_ops.assign_add(max_var, batch_max, name='update')
with ops.name_scope(None, 'total', (average_precision,)) as total_scope:
total_var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=total_scope)
batch_total = math_ops.reduce_sum(average_precision, name='batch_total')
total_update = state_ops.assign_add(total_var, batch_total, name='update')
# Divide total by max to get mean, for both vars and the update ops.
| tensorflow.python.ops.math_ops.reduce_sum | 10,481 |
import tensorflow as tf
size=0,
dynamic_size=True,
element_shape=(facts[:, 0, :].get_shape()))
_, output_op, _ = tf.while_loop(cond, body, [facts, output_ta, 0])
self_attention = output_op.stack()
self_attention = tf.transpose(self_attention, perm = [1, 0, 2])
return self_attention
def self_all_attention(facts, ATTENTION_SIZE, mask, stag='null'):
if len(facts.get_shape().as_list()) == 2:
facts = tf.expand_dims(facts, 1)
def cond(batch, output, i):
return tf.less(i, tf.shape(batch)[1])
def body(batch, output, i):
self_attention_tmp = din_fcn_attention(batch[:, i, :], batch,
ATTENTION_SIZE, mask, softmax_stag=1, stag=stag,
mode='LIST')
self_attention_tmp = tf.reduce_sum(self_attention_tmp, 1)
output = output.write(i, self_attention_tmp)
return batch, output, i + 1
output_ta = tf.TensorArray(dtype=tf.float32,
size=0,
dynamic_size=True,
| tensorflow.shape | 10,482 |
import tensorflow as tf
embedding = make_convolutions(self.char_embedding)
# for highway and projection layers
n_highway = cnn_options.get('n_highway')
use_highway = n_highway is not None and n_highway > 0
use_proj = n_filters != projection_dim
if use_highway or use_proj:
# reshape from (batch_size, n_tokens, dim) to (-1, dim)
batch_size_n_tokens = tf.shape(embedding)[0:2]
embedding = tf.reshape(embedding, [-1, n_filters])
# set up weights for projection
if use_proj:
assert n_filters > projection_dim
with tf.variable_scope('CNN_proj') as scope:
W_proj_cnn = tf.get_variable(
"W_proj", [n_filters, projection_dim],
initializer=tf.random_normal_initializer(
| tensorflow.shape | 10,483 |
import tensorflow as tf
net = tf.layers.dense(net, 200, activation=tf.nn.relu,
kernel_initializer=init_w, bias_initializer=init_b, name='l2', trainable=trainable)
with tf.variable_scope('a'):
actions = tf.layers.dense(net, self.a_dim, activation=tf.nn.tanh, kernel_initializer=init_w,
bias_initializer=init_b, name='a', trainable=trainable)
| tensorflow.variable_scope | 10,484 |
import tensorflow as tf
heatmap_pred = tf.squeeze(heatmap_pred_list[i])
heatmap_true = tf.squeeze(heatmap_true_list[i])
loss = 0.5 * tf.losses.mean_squared_error(y_pred=heatmap_pred * true_weight[:, i],
y_true=heatmap_true * true_weight[:, i])
losses.append(loss)
return tf.reduce_mean(loss)
class JointsMSELoss(object):
def __init__(self):
| tensorflow.reduce_mean | 10,485 |
import tensorflow as tf
Returns
Tensor with same shape as bboxes but making sure that none
of the bboxes are outside the image.
"""
with tf.name_scope('BoundingBoxTransform/clip_bboxes'):
bboxes = tf.cast(bboxes, dtype=tf.float32)
imshape = tf.cast(imshape, dtype=tf.float32)
x1, y1, x2, y2 = tf.split(bboxes, 4, axis=1)
width = imshape[1]
| tensorflow.cast | 10,486 |
import tensorflow as tf
args = parser.parse_args()
def model():
x = tf.placeholder(tf.float32, [None, 784], name='x')
gt = tf.placeholder(tf.float32, [None, 10], name='groundtruth')
with tf.variable_scope('layer1'):
w1 = tf.get_variable('weight1', [784, 1024], initializer=tf.random_normal_initializer())
b1 = tf.get_variable('bias1', [1024], initializer=tf.constant_initializer(0.0))
h1 = tf.nn.relu(tf.matmul(x, w1) + b1)
with tf.variable_scope('layer2'):
w2 = tf.get_variable('weight2', [1024, 1024], initializer=tf.random_normal_initializer())
b2 = tf.get_variable('bias2', [1024], initializer=tf.constant_initializer(0.0))
h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
with tf.variable_scope('layer3'):
w3 = tf.get_variable('weight3', [1024, 10], initializer=tf.random_normal_initializer())
b3 = tf.get_variable('bias3', [10], initializer=tf.constant_initializer(0.0))
y = tf.matmul(h2, w3) + b3
# losses
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=gt, logits=y))
# optimizer
optimizer = tf.train.GradientDescentOptimizer(args.lr)
# define one-step train ops
train_op = optimizer.minimize(cross_entropy)
return x, y, gt, train_op
| tensorflow.matmul | 10,487 |
import tensorflow as tf
The 'crop' mode requires source images to be at least
as large as the network input size,
while the other modes support any sizes and apply
random bbox distortions
before resizing (even with --nodistortions).""")
tf.flags.DEFINE_boolean('distortions', True,
"""Enable/disable distortions during
image preprocessing. These include bbox and color
distortions.""")
tf.flags.DEFINE_string('local_parameter_device', 'gpu',
"""Device to use as parameter server: cpu or gpu.
For distributed training, it can affect where caching
of variables happens.""")
tf.flags.DEFINE_string('device', 'gpu',
"""Device to use for computation: cpu or gpu""")
#tf.flags.DEFINE_string('data_format', 'NCHW',
tf.flags.DEFINE_string('data_format', 'NHWC',
"""Data layout to use: NHWC (TF native)
or NCHW (cuDNN native).""")
tf.flags.DEFINE_integer('num_intra_threads', 1,
"""Number of threads to use for intra-op
parallelism. If set to 0, the system will pick
an appropriate number.""")
tf.flags.DEFINE_integer('num_inter_threads', 0,
"""Number of threads to use for inter-op
parallelism. If set to 0, the system will pick
| tensorflow.flags.DEFINE_string | 10,488 |
import tensorflow as tf
tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif)
pred_posi_dif = tf.where(geq, pred_dif, -pred_dif)
loss = tf.maximum(0., tgt_posi_dif - pred_posi_dif)
cstr_pct = tf.math.count_nonzero(loss, dtype=tf.float32) / tf.cast(tf.reduce_prod(tf.shape(loss)), tf.float32)
final_loss = tf.reduce_mean(loss)
return final_loss, cstr_pct
def contra_traj_lossV7(pred, tgt, horizon=12, temp=100):
horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon)
| tensorflow.reduce_mean | 10,489 |
import tensorflow as tf
vf_old, vf_old_params, _, _ = self.build_cnet(batch['state'], 'oldvf')
self.vf, vf_params, self.vf_state_init, self.vf_state_final = self.build_cnet(batch['state'], 'vf')
self.vf_eval, _, self.vf_eval_state_init, self.vf_eval_state_final = self.build_cnet(self.state, 'vf', reuse=True, batch_size=1)
self.sample_action = tf.squeeze(pi_eval.sample(1), axis=0)
self.eval_action = pi_eval.mode()
self.global_step = tf.train.get_or_create_global_step()
self.saver = tf.train.Saver()
# Loss functions and training
epsilon_decay = tf.train.polynomial_decay(self.EPSILON, self.global_step, self.EPS_LEN, 0.1, power=1)
ratio = tf.maximum(pi.prob(batch['actions']), 1e-6) / tf.maximum(pi_old.prob(batch['actions']), 1e-6)
ratio = tf.clip_by_value(ratio, 0, 10)
surr1 = batch['advantage'] * ratio
surr2 = batch['advantage'] * tf.clip_by_value(ratio, 1 - epsilon_decay, 1 + epsilon_decay)
loss_pg = - 2.0 * tf.reduce_mean(tf.minimum(surr1, surr2))
loss_vf = 0.5 * tf.reduce_mean(tf.square(batch['rewards'] - self.vf))
loss_entropy = - 0.01 * tf.reduce_mean(pi.entropy())
loss = loss_pg + loss_vf + loss_entropy
opt = tf.train.AdamOptimizer(self.LR)
self.train_op = opt.minimize(loss, global_step=self.global_step, var_list=pi_params + vf_params)
self.pi_new_params = [oldp.assign(p) for p, oldp in zip(pi_params, pi_old_params)]
self.vf_new_params = [oldp.assign(p) for p, oldp in zip(vf_params, vf_old_params)]
self.sess.run(tf.global_variables_initializer())
# Tensorboard
if summary_dir is not None:
| tensorflow.clip_by_value | 10,490 |
import tensorflow as tf
def build_trainer(self, child_model):
child_model.build_valid_rl()
self.valid_acc = (tf.to_float(child_model.valid_shuffle_acc) /
tf.to_float(child_model.batch_size))
self.reward = self.valid_acc
if self.entropy_weight is not None:
self.reward += self.entropy_weight * self.sample_entropy
self.sample_log_prob = tf.reduce_sum(self.sample_log_prob)
self.baseline = tf.Variable(0.0, dtype=tf.float32, trainable=False)
baseline_update = tf.assign_sub(
self.baseline, (1 - self.bl_dec) * (self.baseline - self.reward))
with tf.control_dependencies([baseline_update]):
self.reward = tf.identity(self.reward)
self.loss = self.sample_log_prob * (self.reward - self.baseline)
self.train_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="train_step")
tf_variables = [var for var in tf.trainable_variables() if var.name.startswith(self.name)]
print("-" * 80)
for var in tf_variables:
print(var)
self.train_op, self.lr, self.grad_norm, self.optimizer = get_train_ops(
self.loss,
tf_variables,
self.train_step,
| tensorflow.control_dependencies | 10,491 |
import tensorflow as tf
return tf.exp(-s/2) * (1 + 3.5156229*t**2 + 3.0899424*t**4 + 1.2067492*t**6 + 0.2659732*t**8
+ 0.0360768*t**10 + 0.0045813*t**12)
def __phi_g(s):
t = s/7.5
return tf.sqrt(2/s) * (0.39894228 + 0.01328592*t**(-1) + 0.00225319*t**(-2) - 0.00157565*t**(-3)
+ 0.0091628*t**(-4) - 0.02057706*t**(-5) + 0.02635537*t**(-6) - 0.01647633*t**(-7)
+ 0.00392377*t**(-8))
a = 7.5
return __phi_f(tf.minimum(x, a)) - __phi_f(a) + __phi_g(tf.maximum(x, a))
N = tf.cast(tf.shape(X)[0], tf.float32)
if y is None:
y = silverman_rule_of_thumb(N)
A = 1/(N*N*tf.sqrt(y))
B = 2.0/(N*tf.sqrt(y+0.5))
A1 = euclidean_norm_squared(tf.subtract(tf.expand_dims(X, 0), tf.expand_dims(X, 1)), axis=2)/(4*y)
B1 = euclidean_norm_squared(X, axis=1)/(2+4*y)
return 1/tf.sqrt(1+y) + A*tf.reduce_sum(__phi(A1)) - B*tf.reduce_sum(__phi(B1))
| tensorflow.shape | 10,492 |
import tensorflow as tf
elif encoder.final_state == 'average':
mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_outputs_)[1], dtype=tf.float32)
mask = tf.expand_dims(mask, axis=2)
encoder_state_ = tf.reduce_sum(mask * encoder_outputs_, axis=1) / tf.reduce_sum(mask, axis=1)
elif encoder.final_state == 'average_inputs':
mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_inputs_)[1], dtype=tf.float32)
mask = tf.expand_dims(mask, axis=2)
encoder_state_ = tf.reduce_sum(mask * encoder_inputs_, axis=1) / tf.reduce_sum(mask, axis=1)
elif encoder.bidir and encoder.final_state == 'last_both':
encoder_state_ = tf.concat([last_forward, last_backward], axis=1)
elif encoder.final_state == 'none':
encoder_state_ = tf.zeros(shape=[batch_size, 0])
| tensorflow.expand_dims | 10,493 |
import tensorflow as tf
def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0):
with tf.variable_scope(scope):
nin = x.get_shape()[1].value
w = tf.get_variable("w", [nin, nh], initializer=ortho_init(init_scale))
b = tf.get_variable("b", [nh], initializer=tf.constant_initializer(init_bias))
return tf.matmul(x, w)+b
def batch_to_seq(h, nbatch, nsteps, flat=False):
if flat:
h = tf.reshape(h, [nbatch, nsteps])
else:
h = tf.reshape(h, [nbatch, nsteps, -1])
return [tf.squeeze(v, [1]) for v in tf.split(axis=1, num_or_size_splits=nsteps, value=h)]
def seq_to_batch(h, flat = False):
shape = h[0].get_shape().as_list()
if not flat:
assert(len(shape) > 1)
nh = h[0].get_shape()[-1].value
return tf.reshape(tf.concat(axis=1, values=h), [-1, nh])
else:
return tf.reshape(tf.stack(values=h, axis=1), [-1])
def lstm(xs, ms, s, scope, nh, init_scale=1.0):
| tensorflow.squeeze | 10,494 |
import tensorflow as tf
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
| tensorflow.contrib.tpu.TPUEstimator | 10,495 |
import tensorflow as tf
else:
grads = tf.gradients(loss, self.params)
| tensorflow.gradients | 10,496 |
import tensorflow as tf
X = tf.reshape(X, (-1, w // stride, h // stride, ch)) # Sanity shape check
return X
####################################
# Utils
####################################
def _do_cutout(self, image, im_width, im_height, cutout_size):
mask = tf.ones([cutout_size, cutout_size], dtype=tf.int32)
start_x = tf.random.uniform(shape=(1,), minval=0, maxval=im_width, dtype=tf.int32)
start_y = tf.random.uniform(shape=(1,), minval=0, maxval=im_height, dtype=tf.int32)
mask = tf.pad(mask, [[cutout_size + start_y[0], im_height - start_y[0]],
[cutout_size + start_x[0], im_width - start_x[0]]])
mask = mask[cutout_size: cutout_size + im_height,
cutout_size: cutout_size + im_width]
mask = tf.tile(tf.reshape(mask, (im_height, im_width, 1)), (1, 1, 3))
image = tf.where(tf.equal(mask, 0), x=image, y=tf.zeros_like(image))
return image
def _add_drop_path(self, X, keep_prob):
with tf.variable_scope('drop_path'):
| tensorflow.random.uniform | 10,497 |
import tensorflow as tf
def get_input_function():
"""A function to get test inputs. Returns an image with one box."""
image = tf.random_uniform([32, 32, 3], dtype=tf.float32)
key = tf.constant('image_000000')
class_label = tf.random_uniform(
[1], minval=0, maxval=NUMBER_OF_CLASSES, dtype=tf.int32)
box_label = tf.random_uniform(
| tensorflow.constant | 10,498 |
import tensorflow as tf
with slim.arg_scope([slim.conv2d], activation_fn=None,
normalizer_fn=None, normalizer_params=None):
box_encodings = slim.conv2d(
net, num_predictions_per_location * self._box_code_size,
[self._kernel_size, self._kernel_size],
scope='BoxEncodingPredictor')
if self._use_dropout:
net = slim.dropout(net, keep_prob=self._dropout_keep_prob)
class_predictions_with_background = slim.conv2d(
net, num_predictions_per_location * num_class_slots,
[self._kernel_size, self._kernel_size], scope='ClassPredictor',
biases_initializer=tf.constant_initializer(
self._class_prediction_bias_init))
if self._apply_sigmoid_to_scores:
class_predictions_with_background = tf.sigmoid(
class_predictions_with_background)
combined_feature_map_shape = shape_utils.combined_static_and_dynamic_shape(
image_features)
box_encodings = tf.reshape(
box_encodings, tf.stack([combined_feature_map_shape[0],
combined_feature_map_shape[1] *
| tensorflow.constant_initializer | 10,499 |
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